Wireless radio access technologies continue to be improved to handle increased data volumes and larger numbers of subscribers. The 3GPP organization is developing a new radio system (commonly referred to at this time as 5th Generation/5G) to handle peak data rates of the order of ˜10 Gbps (gigabits per second) while still satisfying ultra-low latency requirements in existence for certain 4G applications. 5G intends to utilize radio spectrum on the order of GHz or more in the millimeter-wave (mmWave) band; and also to support massive MIMO (m-MIMO). M-MIMO systems are characterized by a much larger number of antennas as compared to 4G systems, as well as finer beamforming and a higher antenna gain.
FIG. 1 is a schematic overview of an example 5G radio environment in which these teachings may be deployed. Rather than a conventional unitary cellular base station such as a typical eNB of an E-UTRAN system, the 5G system is to have the conventional base station's functionality distributed among a baseband unit (BBU, which may be implemented as a single BBU or multiple interconnected BBUs) and one or typically multiple remote radio heads (RRHs) each located up to a few kilometers from the BBU. For more generality beyond only 5G, FIG. 1 shows a Master 20 that would be the BBU in a 5G system and multiple antennas (ANTs) 30 that would be the RRHs in a 5G network. Each ANT/RRH 30 is operationally connected to its Master/BBU 20 via a wired or wireless bidirectional transmission link which may be known in 5G as a front haul (FH) link. The BBU/RRH combination in 5G systems may be referred to as a gNB. On the uplink these multiple ANTs/RRHs 30 are the multi-points sending messages to the Master/BBU 20 that is in the position of point in multipoint-to-point communications. A given UE (not shown at FIG. 1) may have active connections with one or more of the ANTs/RRHs 30, which in the 5G system would be operating as a transmission/reception point (TRP) of the gNB. There is a somewhat similar distribution of access node functionality in cloud-based radio access networks (C-RAN) that are currently being deployed in at least some LTE networks, though those systems typically use a different terminology than BBU and RRH. 5G and C-RAN are but two non-limiting radio environments that utilize multipoint-to-point communications in which these teachings may be practiced to advantage.
From the perspective of the point (Master 20 in FIG. 1), these continuous streams of data traffic from distributed sources must be gathered and processed. Among these streams different messages have different latency requirements and be of different message types. In any kind of distributed system that involves processing streams of real-time traffic from multiple sources based on strict deadlines, the challenge is to combine the data received from such parallel streams and process it based on the intended deadlines. For example, in the LTE system messages are exchanged on a per-transmit-time-interval (TTI) basis and so their transmit deadlines will be at the TTI boundary meaning processing must begin sometime in advance of that boundary to meet the transmit deadline. Such challenges are commonplace in systems such as in Single Frequency Networks (SFN) where access points are distributed and are constantly streaming the data traffic to some centrally-located unit for further combining and processing. With highly distributed systems like 5G and C-RAN the problem of dealing with multiple parallel data streams which require correlating data received from separate data streams within the strict processing deadlines becomes even more complex.
With regards to the SFN, the layer 2 processing of the wireless networking protocol stack resides on the centrally located Master 20 whereas either both of the layer 1 processing and the antennas or just the antennas are spread out as the ANTs so as to each form a node in a multipoint distribution to provide the wireless coverage over a larger area. Generally layer 1 represents RF level signal processing and layer 2 represents baseband level processing, but in different systems and even among current discussions for 5G (due to beamforming considerations) there is a wide range of how much processing is to be done by the ANTs 30 prior to forwarding the signal to the Master 20. Regardless, the data from each of the distributed ANTs 30 is received by Master 20 continuously and processed there based on the timing information that is present in each received message. The data stream received from each ANT 30 typically cannot be processed independently of the data stream received from other ANTs, for example because the data pertinent to a single user could be received from multiple ANTs in which case it must be correlated from the different streams. Similarly, signal strength measurements received from each data stream from each ANT has to be compared.
The data packets received at the Master 20 from the ANTs 30 have a timestamp carrying timing information that helps the Master 20 to organize the different data packets based on time and also to handle timeouts and retransmissions. This timing information in different messages from different streams and different sources is correlated when messages from different streams need to be correlated, and this timing information is also compared against the Master's system clock to ensure the latency deadline of each message is met.
Data packet correlation among different continuous streams using timing as well as packet content is quite challenging in SFN-like systems because it must be done within strict time limits to avoid packet delay or discard further downstream. Previous solutions to this problem dealt with a much smaller number of data streams and much less volume of traffic than is anticipated for 5G and C-RAN deployments, and those previous solutions are not seen to be reasonably adaptable to meet this new challenge. This is because most of the distributed multipoint-to-point messaging techniques involve processing each stream received at the master independently and increases to the number of streams could be handled by simple scaling of networking and computing resources. In SFN-like systems such as 5G and C-RAN the assumption that the data streams can be handled independently does not hold. As the volume of traffic being received at the master scales with the number of distributed access points, the complexity of handling all those data streams also increases and merely adding parallel processing capacity addresses the volume increase but not the complexity increase that arises from correlating messages received on different streams.
The solution presented herein addresses this problem of processing and combining messages at the receiver's (master's) end in systems that involve Multipoint-to-Point real-time traffic, where the messages are of different types, come from different distributed sources, carry different deadlines/latency requirements, and may need to be correlated across streams.